{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import xgboost as xgb\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "f_score = joblib.load('./f_score')\n",
    "cols = f_score[f_score!=0].index\n",
    "\n",
    "len(cols)\n",
    "\n",
    "x_train = joblib.load('x_trian.lz4')\n",
    "y_train = joblib.load('y_train.lz4')\n",
    "\n",
    "x_train_fs = x_train[cols]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n",
      "[0]\ttrain-auc:0.821262+0.00370416\ttest-auc:0.764056+0.00343687\n",
      "[40]\ttrain-auc:0.903172+0.000624216\ttest-auc:0.822193+0.00219195\n",
      "[80]\ttrain-auc:0.925247+0.0011678\ttest-auc:0.833951+0.0022198\n",
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      "[240]\ttrain-auc:0.972673+0.000555468\ttest-auc:0.847201+0.000892839\n",
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      "[320]\ttrain-auc:0.984703+0.00042458\ttest-auc:0.848151+0.000372309\n",
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      "[80]\ttrain-auc:0.935613+0.00111211\ttest-auc:0.833189+0.00262017\n",
      "[120]\ttrain-auc:0.952979+0.0011327\ttest-auc:0.83998+0.00276714\n",
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      "1000\n",
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      "1600\n",
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      "1700\n",
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      "[120]\ttrain-auc:0.970014+0.000933333\ttest-auc:0.835837+0.00207292\n",
      "[160]\ttrain-auc:0.982593+0.000582952\ttest-auc:0.839641+0.00208714\n",
      "[200]\ttrain-auc:0.990717+0.000697399\ttest-auc:0.842335+0.00204957\n",
      "[240]\ttrain-auc:0.99524+0.000430179\ttest-auc:0.843596+0.00207161\n",
      "[280]\ttrain-auc:0.99775+0.000194673\ttest-auc:0.844589+0.00239575\n",
      "[320]\ttrain-auc:0.998993+0.000140884\ttest-auc:0.845591+0.00257235\n",
      "[360]\ttrain-auc:0.999562+8.40529e-05\ttest-auc:0.846318+0.0026217\n",
      "[399]\ttrain-auc:0.999817+4.3492e-05\ttest-auc:0.847015+0.00274726\n",
      "1800\n",
      "[0]\ttrain-auc:0.822847+0.000875459\ttest-auc:0.755169+0.000880803\n",
      "[40]\ttrain-auc:0.924516+0.00060882\ttest-auc:0.817588+0.00336753\n",
      "[80]\ttrain-auc:0.952817+0.00181967\ttest-auc:0.82888+0.00260359\n",
      "[120]\ttrain-auc:0.9709+0.00122922\ttest-auc:0.836078+0.00195731\n",
      "[160]\ttrain-auc:0.983105+0.000936129\ttest-auc:0.839924+0.00174568\n",
      "[200]\ttrain-auc:0.990922+0.000618506\ttest-auc:0.842631+0.00158477\n",
      "[240]\ttrain-auc:0.995502+0.000259071\ttest-auc:0.844256+0.00194716\n"
     ]
    }
   ],
   "source": [
    "for i in range(100,len(cols)+100,100):\n",
    "    print(i)\n",
    "    x = x_train_fs.iloc[:,:i].values\n",
    "    y = y_train\n",
    "    params={\n",
    "        'booster':'gbtree',\n",
    "        'objective': 'binary:logistic',\n",
    "        'early_stopping_rounds':10,\n",
    "        'scale_pos_weight': float(len(y)-np.sum(y))/float(np.sum(y)),  # 负例样本除以正例样本\n",
    "        'eval_metric': 'auc',\n",
    "        'gamma':0.1,\n",
    "        'max_depth':6,\n",
    "        'lambda':100,\n",
    "        'subsample':0.9,\n",
    "        'colsample_bytree':0.9,\n",
    "        'eta': 0.04,\n",
    "        'seed':2018,\n",
    "        'nthread':18\n",
    "            }\n",
    "    dtrain = xgb.DMatrix(x,y)\n",
    "    eval_hist = xgb.cv(params,dtrain,num_boost_round=400,nfold=3,verbose_eval =40)\n",
    "    joblib.dump(eval_hist,'./rough_eval/eval_hist_{}'.format(i))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 展示test_auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_names = os.listdir('./rough_eval/')\n",
    "file_names = sorted(file_names,key=lambda x:int(x.split('_')[-1]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "auc_df = pd.DataFrame()\n",
    "num_list = []\n",
    "auc_mean_list = []\n",
    "auc_std_list = []\n",
    "for file in file_names:\n",
    "    num_list.append(int(file.split('_')[-1]))\n",
    "    tmp = joblib.load('./rough_eval/{}'.format(file)).iloc[-1,[0,1]]\n",
    "    auc_mean_list.append(tmp['test-auc-mean'])\n",
    "    auc_std_list.append(tmp['test-auc-std'])\n",
    "auc_df['num'] = num_list\n",
    "auc_df['test-auc-mean'] = auc_mean_list\n",
    "auc_df['test-auc-std'] = auc_std_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd2ed0515c0>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(auc_df['num'],auc_df['test-auc-mean'])\n",
    "plt.figure()\n",
    "plt.plot(auc_df['num'],auc_df['test-auc-std'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
